집값예측_안심향.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ansimhyang60/Project/blob/main/%EC%A7%91%EA%B0%92%EC%98%88%EC%B8%A1_%EC%95%88%EC%8B%AC%ED%96%A5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[데이터 설명]\n",
        " id : 데이터 고유 id\n",
        "\n",
        "OverallQual : 전반적 재료와 마감 품질\n",
        "\n",
        "YearBuilt : 완공 연도\n",
        "\n",
        "YearRemodAdd : 리모델링 연도\n",
        "\n",
        "ExterQual : 외관 재료 품질\n",
        "\n",
        "BsmtQual : 지하실 높이\n",
        "\n",
        "TotalBsmtSF : 지하실 면적\n",
        "\n",
        "1stFlrSF : 1층 면적\n",
        "\n",
        "GrLivArea : 지상층 생활 면적\n",
        "\n",
        "FullBath : 지상층 화장실 개수\n",
        "\n",
        "KitchenQual : 부억 품질\n",
        "\n",
        "GarageYrBlt : 차고 완공 연도\n",
        "\n",
        "GarageCars: 차고 자리 개수\n",
        "\n",
        "GarageArea: 차고 면적\n",
        "\n",
        "target : 집값(달러 단위)"
      ],
      "metadata": {
        "id": "A1Agt4Ill3sy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lshXRhJ7K94i"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use(\"ggplot\")\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('train.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "crZVJ6F5dtnI",
        "outputId": "f1282579-4b30-4844-b5ea-ff9081597c0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-40e11b4a-9b9a-4aa0-83ed-aac7d6696e2d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Overall Qual</th>\n",
              "      <th>Gr Liv Area</th>\n",
              "      <th>Exter Qual</th>\n",
              "      <th>Garage Cars</th>\n",
              "      <th>Garage Area</th>\n",
              "      <th>Kitchen Qual</th>\n",
              "      <th>Total Bsmt SF</th>\n",
              "      <th>1st Flr SF</th>\n",
              "      <th>Bsmt Qual</th>\n",
              "      <th>Full Bath</th>\n",
              "      <th>Year Built</th>\n",
              "      <th>Year Remod/Add</th>\n",
              "      <th>Garage Yr Blt</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2392</td>\n",
              "      <td>Ex</td>\n",
              "      <td>3</td>\n",
              "      <td>968</td>\n",
              "      <td>Ex</td>\n",
              "      <td>2392</td>\n",
              "      <td>2392</td>\n",
              "      <td>Ex</td>\n",
              "      <td>2</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>386250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>1352</td>\n",
              "      <td>Gd</td>\n",
              "      <td>2</td>\n",
              "      <td>466</td>\n",
              "      <td>Gd</td>\n",
              "      <td>1352</td>\n",
              "      <td>1352</td>\n",
              "      <td>Ex</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>2007</td>\n",
              "      <td>2006</td>\n",
              "      <td>194000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>900</td>\n",
              "      <td>TA</td>\n",
              "      <td>1</td>\n",
              "      <td>288</td>\n",
              "      <td>TA</td>\n",
              "      <td>864</td>\n",
              "      <td>900</td>\n",
              "      <td>TA</td>\n",
              "      <td>1</td>\n",
              "      <td>1967</td>\n",
              "      <td>1967</td>\n",
              "      <td>1967</td>\n",
              "      <td>123000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1174</td>\n",
              "      <td>TA</td>\n",
              "      <td>2</td>\n",
              "      <td>576</td>\n",
              "      <td>Gd</td>\n",
              "      <td>680</td>\n",
              "      <td>680</td>\n",
              "      <td>TA</td>\n",
              "      <td>1</td>\n",
              "      <td>1900</td>\n",
              "      <td>2006</td>\n",
              "      <td>2000</td>\n",
              "      <td>135000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1958</td>\n",
              "      <td>Gd</td>\n",
              "      <td>3</td>\n",
              "      <td>936</td>\n",
              "      <td>Gd</td>\n",
              "      <td>1026</td>\n",
              "      <td>1026</td>\n",
              "      <td>Gd</td>\n",
              "      <td>2</td>\n",
              "      <td>2005</td>\n",
              "      <td>2005</td>\n",
              "      <td>2005</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40e11b4a-9b9a-4aa0-83ed-aac7d6696e2d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-40e11b4a-9b9a-4aa0-83ed-aac7d6696e2d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-40e11b4a-9b9a-4aa0-83ed-aac7d6696e2d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   id  Overall Qual  Gr Liv Area  ... Year Remod/Add  Garage Yr Blt  target\n",
              "0   1            10         2392  ...           2003           2003  386250\n",
              "1   2             7         1352  ...           2007           2006  194000\n",
              "2   3             5          900  ...           1967           1967  123000\n",
              "3   4             5         1174  ...           2006           2000  135000\n",
              "4   5             7         1958  ...           2005           2005  250000\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.copy()"
      ],
      "metadata": {
        "id": "0JvJt_ZAMkjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gg-bxfA1Mqfi",
        "outputId": "a51ff0b8-5964-4460-d705-7e9e97604b0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                 int64\n",
              "Overall Qual       int64\n",
              "Gr Liv Area        int64\n",
              "Exter Qual        object\n",
              "Garage Cars        int64\n",
              "Garage Area        int64\n",
              "Kitchen Qual      object\n",
              "Total Bsmt SF      int64\n",
              "1st Flr SF         int64\n",
              "Bsmt Qual         object\n",
              "Full Bath          int64\n",
              "Year Built         int64\n",
              "Year Remod/Add     int64\n",
              "Garage Yr Blt      int64\n",
              "target             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 컬럼 조회"
      ],
      "metadata": {
        "id": "KexvO980jAF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 실수형 컬럼의 조회 (진정한 연속형 데이터 일 경우가 많음)\n",
        "float_data = df.columns[(df.dtypes == float)]\n",
        "print(float_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TijQv95cNKa0",
        "outputId": "08519606-86a4-4c20-e902-6de2884cefd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 정수형 컬럼의 조회\n",
        "# : 연속형 컬럼일 수도 있고\n",
        "# 타이타닉의 사망0 생존 1 경우 처럼 범주형인데 수치로 나타낸 경우도 있음\n",
        "\n",
        "# 이상치 제거는 연속형 데이터에 이상치를 제거하고\n",
        "# 연속형 데이터 중 성인 남성의 키 에서 하승진 선수의 경우 매우 드문 경우 이므로 제거\n",
        "\n",
        "# 타이타닉의 생존 사망과 같은 범주형 데이터는\n",
        "#  0, 1, 2 ,100 과 같은 경우 100 이 이상치 이지만\n",
        "# 100 이라는것이 연속형 데이터 처럼 크다 작다의 의미가 아니라 그룹의 이름이기 때문에\n",
        "# 때문에 보통 이상치 제거를 하지 않음\n",
        "\n",
        "# 이상치 제거시 어느정도 데이터 손실이 일어나기 때문에 이데이터가\n",
        "# 연속형인지 아니면 범주형인지에 대해서 고민후 작업이 이루어 져야함\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "id : 데이터 고유 id\n",
        "\n",
        "OverallQual : 전반적 재료와 마감 품질\n",
        "GrLivArea : 지상층 생활 면적\n",
        "GarageCars: 차고 자리 개수\n",
        "GarageArea: 차고 면적\n",
        "TotalBsmtSF : 지하실 면적\n",
        "1stFlrSF : 1층 면적\n",
        "FullBath : 지상층 화장실 개수\n",
        "YearBuilt : 완공 연도\n",
        "YearRemodAdd : 리모델링 연도\n",
        "GarageYrBlt : 차고 완공 연도\n",
        "ExterQual : 외관 재료 품질\n",
        "BsmtQual : 지하실 높이\n",
        "KitchenQual : 부억 품질\n",
        "target : 집값(달러 단위)\n",
        "\"\"\"\n",
        "\n",
        "int_data = df.columns[(df.dtypes == int)]\n",
        "print(int_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfCfVh-ZfU2w",
        "outputId": "f2fb94e1-63a1-41ea-c078-b11bfe434f88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['id', 'Overall Qual', 'Gr Liv Area', 'Garage Cars', 'Garage Area',\n",
            "       'Total Bsmt SF', '1st Flr SF', 'Full Bath', 'Year Built',\n",
            "       'Year Remod/Add', 'Garage Yr Blt', 'target'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 차고 자리 개수\n",
        "# 연속형의 숫자로 볼 수 도 있지만 범주형으로 볼수도 있는 애매함\n",
        "\n",
        "# 집에 외제차랑 SUV 가 많아서 차고 자리가 작음 -> 집이 비싸지 않을까?\n",
        "# 집이 작아서 차고 자리 1개 -> 집이 싸지 않을까?\n",
        "\n",
        "# 아파트 혹은 연립(외국에는 저소득층 주거지) 이라서 차고 자리가 많을수도\n",
        "# 단순히 이상치 제거하지 말고 고민 필요\n",
        "\n",
        "# 연속형인가? 범주형인가?\n",
        "\n",
        "# 빈도수를 조회하면 대부분 1대~3대 사이\n",
        "df[\"Garage Cars\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgJyTwHUP5H7",
        "outputId": "9c796ad1-0860-46c0-c40d-94d4e157dcd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    794\n",
              "1    372\n",
              "3    172\n",
              "4     11\n",
              "5      1\n",
              "Name: Garage Cars, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 차고 면적\n",
        "plt.figure(figsize=(20,10))\n",
        "df[\"Garage Area\"].hist(bins=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "-OopMhhCQBON",
        "outputId": "09cf987f-92d5-4e62-9ef2-e40d97f49f4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0c3fb3f50>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "# 차 1대당 차고 면적\n",
        "# 대부분 200~400 사이\n",
        "(df[\"Garage Area\"]//df[\"Garage Cars\"]).hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "bkCxui5iQPvu",
        "outputId": "f0d8bcc6-4770-4e12-cea2-f024edc7a8b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0c3ecec90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 완공년도\n",
        "# 1900 년대 : 958\n",
        "# 2000 년대 : 381\n",
        "# 1800 년대 : 11 -> 문제의 이상치들 (1800 년대에 지어진 집)\n",
        "\n",
        "(df['Year Built']//100).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mioQQot0TN6w",
        "outputId": "1f52a6a0-0b0a-4543-f0a3-ee0199c6486b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19    958\n",
              "20    381\n",
              "18     11\n",
              "Name: Year Built, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "df['Year Built'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "81LPZ1YTS6Lx",
        "outputId": "c1d3c27c-e9f3-4823-c218-97808c864ffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0c3e6b250>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 년도와 집값의 산점도\n",
        "# 보통 1900년도 이전에 지어진 집이 집값이 쌈\n",
        "# 그러나 1900년도 이전에 지어진 집 중에서 1집의 집값만 비쌈 (유적지? 박물관?) => 이집만 이상치로 보는건 어떤지\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(x=df[\"Year Built\"], y=df[\"target\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "AuKmXm3MT101",
        "outputId": "54986220-e434-428f-a84f-95eb510cb4ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fd0c3d98550>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[box plot Result]\n",
        "\n",
        "Remove-outlier : Garage Cars, Full Bath, Year Built, Garage Yr Blt\n",
        "\n",
        "Change-standard -> Check-outlier : Gr Liv Area, Garage Area, Total Bsmt SF, 1st Fir SF, (target)"
      ],
      "metadata": {
        "id": "SyjEbAnYkaen"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Function : Removing outlier]"
      ],
      "metadata": {
        "id": "x7JxRhyTuuAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n4df = df[['Garage Cars', 'Full Bath', 'Year Built', 'Garage Yr Blt']]"
      ],
      "metadata": {
        "id": "k8rep0xuuYvI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ndata = n4df.columns"
      ],
      "metadata": {
        "id": "W0XVdSDBVWVf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "plt.suptitle(\"Boxplots\", fontsize=40)\n",
        "\n",
        "for i in range(len(ndata)):\n",
        "    plt.subplot(2,2,i+1) # 행/열/개수\n",
        "    plt.title(ndata[i])\n",
        "    plt.boxplot(df[ndata[i]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "outputId": "bfbba0b7-137d-4fac-c1e3-d096877ca9d3",
        "id": "r4-1AnMwuYvF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def outlier(data):\n",
        "  q1, q3 = np.percentile(data, [25,75])\n",
        "  print(\"q1 =\", q1)\n",
        "  print(\"q3 =\", q3)\n",
        "  iqr = q3 - q1 # 중심 범위\n",
        "  lb = q1 - (iqr * 1.5) # 양쪽 0.25 만큼 확장\n",
        "  ub = q3 + (iqr * 1.5)\n",
        "  print(\"lb = \", lb)\n",
        "  print(\"ub =\" , ub)\n",
        "\n",
        "  return data.index[(data > ub) | (data < lb)]"
      ],
      "metadata": {
        "id": "9em4gxyvuYvI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "YB_idx = outlier(n4df['Year Built'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdNYLawoyGWg",
        "outputId": "d7c401ee-29e8-465a-a637-72dd379a60c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "q1 = 1955.0\n",
            "q3 = 2002.0\n",
            "lb =  1884.5\n",
            "ub = 2072.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "YB_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFzeOqj3uzkY",
        "outputId": "5fd53d8c-f780-454e-fc8c-40526dd1208b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([286, 812, 888, 940], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n4df.loc[YB_idx,'Year Built']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWtQrnY2uYvJ",
        "outputId": "e83c66fa-a53e-47c2-ee6f-e99de6aa2990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "286    1882\n",
              "812    1880\n",
              "888    1880\n",
              "940    1880\n",
              "Name: Year Built, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n4df = n4df.loc[set(n4df.index) - set(YB_idx)]"
      ],
      "metadata": {
        "id": "LXDrN2aMuYvM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for i in n4_whole_outlier:\n",
        "#   n4df = n4df.drop(n4df.index[i])\n",
        "n4df[n4df['Year Built']<1900]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "3915b354-72f8-49e9-9a3d-344909033c93",
        "id": "wGBDosfiuYvM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3c1a675e-b68e-4848-937e-fa2890f464d6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Garage Cars</th>\n",
              "      <th>Full Bath</th>\n",
              "      <th>Year Built</th>\n",
              "      <th>Garage Yr Blt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1890</td>\n",
              "      <td>1950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1893</td>\n",
              "      <td>1988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1885</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1890</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1890</td>\n",
              "      <td>1963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1892</td>\n",
              "      <td>1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1187</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1885</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c1a675e-b68e-4848-937e-fa2890f464d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c1a675e-b68e-4848-937e-fa2890f464d6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c1a675e-b68e-4848-937e-fa2890f464d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Garage Cars  Full Bath  Year Built  Garage Yr Blt\n",
              "125             2          2        1890           1950\n",
              "201             2          3        1893           1988\n",
              "280             2          2        1885           1971\n",
              "317             2          1        1890           1996\n",
              "467             2          1        1890           1963\n",
              "683             3          2        1892           1993\n",
              "1187            1          1        1885           1954"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "YB_idx = outlier(n4df['Year Built'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_U0EDA2y0YRR",
        "outputId": "d929fbd3-c99b-4568-a1c1-9a51ca74c3ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "q1 = 1956.0\n",
            "q3 = 2002.0\n",
            "lb =  1887.0\n",
            "ub = 2071.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,6))\n",
        "boxplot = n4df.boxplot(column=['Year Built'])\n",
        "#plt.yticks(np.arange(1800,2020,step=20))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "27453017-66ee-4e5f-a202-c4d9b6d68dbc",
        "id": "GBjJKIWduYvM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}